lightbulbPractical problemLLM Prompt Engineering and Authoring Tools
Ordinary users struggle to design effective prompts and optimize LLM output.LLM and Natural Language Interaction / LLM Prompt Engineering and Authoring Tools
Ordinary users struggle to design effective prompts and optimize LLM output.
Similar questions
lightbulbPractical problemLLM Prompt Engineering and Authoring Tools
Users cannot efficiently evaluate and optimize LLM instruction effectiveness in complex tasks.lightbulbPractical problemLLM Prompt Engineering and Authoring Tools
Developers struggle to optimize prompt design, leading to low accuracy in emotion and mental health tasks.lightbulbPractical problemLLM Prompt Engineering and Authoring Tools
Data engineers and business analysts face inconsistent interfaces when integrating and processing complex data.lightbulbPractical problemLLM Prompt Engineering and Authoring Tools
Users' feedback on LLMs is difficult to sustain in model behavior, requiring repeated actions across interactions.lightbulbPractical problemLLM Prompt Engineering and Authoring Tools
Lay users struggle to obtain accurate and easy-to-use help guidance when using complex software.IUI '24Why and When LLM-Based Assistants Fail: Exploring the Effectiveness of Prompt-Based Interaction for Software Help-Seeking
lightbulbPractical problemLLM Prompt Engineering and Authoring Tools
Users struggle to design and optimize LLM prompt chains from scratch because the process is complex and laborious.UIST '24ChainBuddy: An AI-assisted Agent System for Helping Users Set up LLM Pipelines
Related papers
IUI 2025
CoPrompter: User-Centric Evaluation of LM Instruction Alignment for Improved Prompt Engineering
Ishika Joshi, Simra Shahid, Shreeya Manasvi Venneti
CHI 2025
Exploring Modular Prompt Design for Emotion and Mental Health Recognition
Minseo Kim, Taemin Kim, Thu Hoang Anh Vo
IUI 2024
Empirical Evidence on Conversational Control of GUI in Semantic Automation
Daniel Karl I. Weidele, Mauro Martino, Abel N. Valente
IUI 2024
ConstitutionMaker: Interactively Critiquing Large Language Models by Converting Feedback into Principles
Savvas Petridis, Benjamin D Wedin, James Wexler
CHI 2023
Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts
J.D. Zamfirescu-Pereira, Richmond Y. Wong, Bjoern Hartmann
CHI 2022
AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts
Tongshuang Wu, Michael Terry, Carrie J Cai
CHI 2022
Great Chain of Agents: The Role of Metaphorical Representation of Agents in Conversational Crowdsourcing
Ji-Youn Jung, Sihang Qiu, Alessandro Bozzon
CHI 2022
Design Guidelines for Prompt Engineering Text-to-Image Generative Models
Vivian Liu, Lydia B Chilton